55 Trends in opioid- and stimulant-related deaths and hospitalizations among youth (0-19) in Canada from 2018-2023
Bibliographic record
Abstract
Abstract Background Illicit drug use is rising in Canada. Children and youth face particularly unique structural factors that can influence outcomes. The COVID-19 pandemic was especially challenging due to the loss of school structure and peer connections, and increased inaccessibility of health and social services. Recently, overdoses became the leading cause of death of 10-18 year old youths in Western Canada. However, overdose trends in youth are not well characterized. Objectives To determine the rates of accidental opioid- and stimulant-related deaths and hospitalizations for youth in Canada, stratified by sex, from 2018 to 2023. To examine changes in the rate of deaths per hospitalizations. Design/Methods This Canadian population-based study utilized open-access health administrative data from the Public Health Agency of Canada and population estimates from Statistics Canada from 2018 to 2023. Children and youth aged 0-19 years old were included. Accidental opioid- and stimulant-related deaths and hospitalizations per 1,000,000 population were visualized over time. The rates of deaths per hospitalizations were modelled using Poisson regression with an interaction term for sex and year (measured continuously, and as pre-COVID-19 versus during COVID-19). Analyses were completed separately for opioids and stimulants. Results During the study period, there were 526 opioid-related deaths and 977 opioid-related hospitalizations. The rate of opioid-related deaths per hospitalizations was stable among males (rate ratio (RR) 1.04, 95% confidence interval (CI) 0.91-1.16), but increased by 18% per year among females (RR 1.18, 95%CI 1.02-1.35). There were non-significant increases when comparing the periods before and during COVID-19 (RR 1.32, 95%CI 0.70-1.94 for males; RR 1.74, 95%CI 0.76-2.73 for females). There were 220 stimulant-related deaths and 626 stimulant-related hospitalizations. The rate of stimulant-related deaths per hospitalizations was stable among males (RR 1.01, 95%CI 0.85-1.18), but increased by 21% per year among females (RR 1.21, 95%CI 0.97-1.45). There were no significant differences before versus during COVID-19. Conclusion There was a rise in opioid-related and stimulant-related deaths per hospitalizations among females aged 0-19 years old across Canada. Further research should explore why this group is experiencing more lethal outcomes. The effects of COVID-19 were not significant across all groups, possibly due to low power as the dataset only contains two pre-COVID-19 time points. This study also highlights the need for more transparent data reporting across Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".